Parsing CSV with Mixed Data Types in Python - Handling Floats, Strings, and Dates
I've hit a wall trying to I'm stuck on something that should probably be simple. I'm currently working on a project that involves parsing a CSV file with mixed data types, including floats, strings, and dates. My CSV looks something like this: ```csv id,name,value,date 1,Item A,10.5,2023-10-01 2,Item B,15.0,2023-10-02 3,Item C,N/A,2023-10-03 4,Item D,20.0,N/A ``` I’m using Python 3.9 with the `pandas` library to read the CSV file, as it provides convenient tools for data manipulation. Here's the code I’ve implemented so far: ```python import pandas as pd def load_data(file_path): df = pd.read_csv(file_path) return df data = load_data('data.csv') print(data) ``` The scenario arises with the 'value' column, where I have some entries marked as 'N/A'. When I run this code, I get a DataFrame, but the 'value' column is automatically interpreted as strings due to the presence of 'N/A'. This prevents me from performing numerical operations on that column later on. I've tried using `pd.to_numeric(df['value'], errors='coerce')` to convert the values after loading the data, which replaces 'N/A' with `NaN`, but the DataFrame still doesn't have the correct types for further analysis. Here's what I attempted: ```python df['value'] = pd.to_numeric(df['value'], errors='coerce') print(df.dtypes) ``` The output shows that 'value' is cast to float, but I still have to handle the NaN values. Ideally, I want to keep the original string representation of values and handle the 'N/A' entries gracefully without losing data integrity. Is there a better way to pre-process this data while reading it in, or do I need to find a workaround after loading? Any insights would be greatly appreciated! This is part of a larger mobile app I'm building. What would be the recommended way to handle this? My team is using Python for this microservice. Any ideas how to fix this? Is there a simpler solution I'm overlooking?